Work

Spatial-temporal Multi-level Feature Fusion Traffic Flow Prediction Model Based on U-Net Architecture

Year: 2024

Type: conference-paper

Abstract: Traffic flow prediction is important for intelligent transportation systems.Current models extract spatial-temporal features well but static adjacency graphs do not reflect the traffic's dynamics.In this study, we present a novel framework for the deep learning-based traffic flow prediction model, named Spatial-temporal Multi-level Feature Fusion to Traffic Flow Prediction Model (U-STMLFF) in which U-Net architecture is utilized.It employs multi-level pooling for feature extraction and builds dynamic attention adjacency matrices for temporal spatiotemporal learning on graphs with Graph CNNs incorporating a multi-temporal pattern capture module.Attension-based prediction fusion method on a progressive merge scheme improves the prediction accuracy.

Source: International Conference on Computer, Internet of Things and Smart City (CIoTSC)

DOI: 10.1145/3731867.3731902

Authors: Wei-Min Shi, Bo-Xin Yao, Yi-Hua Zhou, Yu-Guang Yang

Institution: Beijing University of Technology

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Funder: National Natural Science Foundation of China